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==CNN architecture==
Due to their number and variety of [[Computer architecture|architectures]], it is difficult to give a precise definition for a CNN processor. From an architecture standpoint, CNN processors are a system of finite, fixed-number, fixed-___location, fixed-topology, locally interconnected, multiple-input, single-output, nonlinear processing units. The nonlinear processing units are often referred to as [[neurons]] or [[cell (biology)|cells]]. Mathematically, each cell can be modeled as a [[Dissipation|dissipative]], nonlinear [[dynamical system]] where information is encoded via its initial state, inputs and variables used to define its behavior. Dynamics are usually continuous, as in the case of [[Discrete time and continuous time|Continuous-Time]] CNN (CT-CNN) processors, but can be discrete, as in the case of [[Discrete time and continuous time|Discrete-Time]] CNN (DT-CNN) processors.<ref>S. Malki, Y. Fuqiang, and L. Spaanenburg, "Vein Feature Extraction Using DT-CNNs", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>H. Harrer and J.Nossek, "Discrete-Time Cellular Neural Networks", [[International Journal of Circuit Theory and Applications]], 20:453-467, 1992.</ref><ref>M. Brugge, "Morphological Design of Discrete−Time Cellular Neural Networks", University of Groningen Dissertation, 2005.</ref>
Each cell has one output, by which it communicates its state with both other cells and external devices. Output is typically [[Real number|real-valued]], but can be [[Complex number|complex]] or even [[quaternion]], i.e. a Multi-Valued CNN (MV-CNN). Most CNN processors, processing units are identical, but there are applications that require non-identical units, which are called Non-Uniform Processor CNN (NUP-CNN) processors, and consist of different types of cells.
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